code: def _evaluate_sequence_prob(
      self, examples: list[SequenceProbExample]
  ) -> dict[str, Sequence[float]]:
      # finding the target
      prompts = [
          example.prompt + example.completion for example in examples
      ]
      tokenized_inputs = self.tokenizer(
          prompts,
          return_tensors="pt",
          truncation=True,
          return_token_type_ids=False,
      ).to(self.device)
  
      target_sequences = [example.completion for example in examples]
      # NOTE: we have to apply the OPT token correction here too
      target_token_lengths = [
          len(self.tokenizer(word)["input_ids"])
          - self.correction_for_start_token
          for word in target_sequences
      ]
  
      outputs = self.model(**tokenized_inputs)
      logits = (
          outputs["logits"]
          .detach()
          .to(device="cpu", dtype=torch.float32)
      )
  
      losses = []
      for i in range(len(examples)):
          # we only need the logits for the end sequence
          tokens = tokenized_inputs["input_ids"][i]
          # we have to go back by one because we don't care about the logits
          # for the predicted token
          sequence_logits = logits[i, -target_token_lengths[i] - 1 : -1]
          sequence_tokens = tokens[-target_token_lengths[i] :]
          logprobs = -F.log_softmax(sequence_logits, dim=-1)
          loss = sum(
              [
                  logprobs[i, token]
                  for i, token in enumerate(sequence_tokens)
              ]
          )
          # type: ignore (the sum is never empty so never just 0, always a
          # tensor)
          losses.append(loss.item())
      return {"loss": losses}
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 6 for calling the HFModel obj's underlying member method code with example usage: obj._evaluate_sequence_prob(examples) and returns: dict